LoRA Visualizer - ComfyUI Custom Node

A ComfyUI custom node that parses prompt text for LoRA tags (wan and image gen, using <lora:yourtag:1.0> syntax and <wanlora:yourtag:1.0> syntax respectively) and visualizes their metadata, including trigger words, strength values, thumbnail previews, and example images.

Features

• ✅ Consistent LoRA Parsing: Backend Python parsing handles both standard <lora:name:strength> and custom <wanlora:name:strength> tags with identical logic
• ✅ Complex Name Support: Handles LoRA names with spaces, colons, and special characters (e.g., <lora:Detail Enhancer v2.0: Professional Edition:0.8>)
• ✅ Visual Thumbnails: Displays actual LoRA preview images loaded from metadata files
• ✅ Metadata Integration: Shows trigger words, model information, and base model details from ComfyUI LoRA Manager
• ✅ Separate Visual Lists: Standard LoRAs (blue theme) and WanLoRAs (orange theme) displayed in distinct, color-coded sections
• ✅ Canvas-based Rendering: Properly integrated with ComfyUI's node system using custom widget drawing
• ✅ Hover Gallery: Hover over thumbnails to see trigger words and example images
• ✅ Backend-Frontend Architecture: Python handles parsing and logic, JavaScript handles visualization
• ✅ Comprehensive Testing: Unit tests cover edge cases and complex name parsing

Installation

1. Clone or download this repository to your ComfyUI custom_nodes directory:

   cd ComfyUI/custom_nodes
   git clone <repository-url> lora-visualizer
   

2. Restart ComfyUI to load the custom node

3. The node will appear in the ComfyUI node menu under conditioning → LoRA Visualizer

Requirements

• ComfyUI LoRA Manager: This node depends on the ComfyUI LoRA Manager custom node being installed, as it uses the metadata files that the LoRA Manager downloads and maintains.

Usage

1. Add the LoRA Visualizer node to your workflow
2. Enter a prompt containing LoRA tags in the prompt_text input field
3. The node will automatically parse and display information about each LoRA found

Supported LoRA Tag Formats

• Standard LoRAs: <lora:model_name:strength>
  • Example: <lora:landscape_v1:0.8>
• WanLoRAs: <wanlora:model_name:strength>
  • Example: <wanlora:Woman877.v2:1.0>

Example Prompt

A beautiful portrait <lora:realistic_skin:0.7> of <wanlora:Woman877.v2:0.8> woman standing in a garden, highly detailed

This will display:

• Standard LoRAs: realistic_skin (strength: 0.7)
• WanLoRAs: Woman877.v2 (strength: 0.8)

Metadata File Requirements

The node looks for metadata files in the models/loras/ directory with the naming pattern:

• {lora_name}.metadata.json
• {lora_name}.safetensors.metadata.json

These files should contain metadata in the format used by the ComfyUI LoRA Manager, including:

• civitai.trainedWords: Array of trigger words
• preview_url: Path to thumbnail image
• civitai.images: Array of example images
• base_model: Base model information
• model_name: Display name of the model

Example Metadata Structure

{
  "file_name": "Woman877.v2",
  "model_name": "Photorealistic AI Influencer – Woman877",
  "preview_url": "/path/to/Woman877.v2.webp",
  "base_model": "SDXL 1.0",
  "civitai": {
    "trainedWords": ["woman877"],
    "images": [
      {
        "url": "https://example.com/image1.jpg",
        "width": 768,
        "height": 1152,
        "nsfwLevel": 1
      }
    ]
  }
}

Output

The node provides two outputs:

1. lora_info (STRING): A formatted text report with detailed information about all found LoRAs
2. processed_prompt (STRING): The original prompt text (can be modified in future versions)

Features in Development

• Image Gallery: Full implementation of hover-to-view example images
• Interactive Controls: Click to copy trigger words, adjust strengths
• Filtering Options: Filter by base model, NSFW level, etc.
• Export Options: Export LoRA information in various formats

Testing

Run the test suite to verify functionality:

./run_tests.sh

File Structure

lora-visualizer/
├── __init__.py                     # Node registration
├── README.md                       # This file
├── nodes/
│   └── lora_visualizer_node.py     # Main node implementation
├── web/
│   └── lora_visualizer.js          # Frontend visualization
└── tests/
    └── test_lora_parsing.py        # Unit tests

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

Publishing to ComfyUI Registry

This node can be published to the ComfyUI Registry for easy installation by users.

Setup for Publishing

1. Create a Publisher Account: Go to Comfy Registry and create a publisher account
2. Get Your Publisher ID: Find your publisher ID (after the @ symbol) on your profile page
3. Update pyproject.toml: Add your Publisher ID to the PublisherId field in pyproject.toml
4. Create API Key: Generate an API key for your publisher in the registry
5. Set GitHub Secret: Add your API key as REGISTRY_ACCESS_TOKEN in your GitHub repository secrets (Settings → Secrets and Variables → Actions → New Repository Secret)

Automated Release Workflow

The project uses conventional commits for automatic semantic versioning. The "Release and Publish" GitHub Action automatically determines the next version based on your commit messages:

Commit Message Format:

• fix: description → patch version bump (1.0.0 → 1.0.1)
• feat: description → minor version bump (1.0.0 → 1.1.0)
• BREAKING CHANGE: in commit body → major version bump (1.0.0 → 2.0.0)

Release Process:

1. Make commits using conventional format
2. Go to Actions → "Release and Publish to ComfyUI Registry"
3. Click "Run workflow"
4. Add changelog (optional)
5. Choose dry run to preview without releasing

This workflow automatically:

• ✅ Analyzes commit messages since last release
• ✅ Calculates appropriate version bump
• ✅ Updates version in pyproject.toml
• ✅ Creates git tag (e.g., v1.1.0)
• ✅ Creates GitHub release with changelog
• ✅ Publishes to ComfyUI Registry

Example Commit Messages:

git commit -m "fix: resolve parsing issue with special characters"
git commit -m "feat: add support for custom LoRA tags"
git commit -m "feat: new visualization mode

BREAKING CHANGE: removes old API methods"

Manual Publishing

For quick republishing without version changes:

1. Go to Actions → "Release and Publish to ComfyUI Registry"
2. Click "Run workflow"
3. Select "publish_only" from the action type dropdown
4. Click "Run workflow"

Alternatively, use the ComfyUI CLI: comfy node publish

For more details, see the ComfyUI Registry Publishing Guide.

Development

Prerequisites

• PDM for dependency management
• Python 3.8+ (same as ComfyUI requirement)

Setup Development Environment

1. Clone the repository:

   git clone https://github.com/oliverswitzer/ComfyUI-Lora-Visualizer.git
   cd ComfyUI-Lora-Visualizer
   

2. Install development dependencies:

   pdm install
   

   This creates a virtual environment and installs pytest, black, and pylint.

Running Tests

pdm run test

Code Quality

Format code with Black:

pdm run format

Lint with Pylint:

pdm run lint

Run all checks (format + lint + test):

pdm run check

Test Structure

• tests/test_lora_parsing.py: Main test suite
• tests/fixtures/: Sample metadata files for testing
• conftest.py: Test configuration and ComfyUI mocking

Tests cover:

• LoRA tag parsing (standard and WanLoRA formats)
• Metadata extraction and processing
• Civitai URL generation
• Edge cases and error handling

Available PDM Scripts

| Command | Description | |---------|-------------| | pdm run format | Format code with Black | | pdm run lint | Lint code with Pylint | | pdm run check | Run format + lint (tests via ./run_tests.sh) |

Note: For tests, use ./run_tests.sh due to import complexities with ComfyUI's module structure.

Adding New Tests

1. Add test methods to TestLoRAVisualizerNode class
2. Use fixture files in tests/fixtures/ for realistic data
3. Mock ComfyUI dependencies (already set up in conftest.py)
4. Run tests to ensure everything passes

Example test:

def test_new_feature(self):
    """Test description."""
    # Setup
    test_data = {...}
    
    # Execute
    result = self.node.some_method(test_data)
    
    # Assert
    self.assertEqual(result, expected_value)

License

[Add your license here]

Changelog

v1.0.0

• Initial release
• Basic LoRA tag parsing
• Metadata visualization
• Thumbnail display
• Separate lists for standard and WanLoRAs